Thanks to evolving data analytics technologies, organizations today no longer have to make significant decisions based on hunches, gut feelings or best guesses.
More and more analytics tools are emerging on the market to help companies dissect their mountains of data to gain business intelligence, using the results to make decisions now and predict the future. Data-driven decisions boost the chances of success and also decrease risks.
The good news is that there’s certainly no lack of data, with global data totals now measured in zettabytes. This number will increase in the coming years as even more connected systems and devices appear on the market.
With these eye-popping numbers in mind, it begs the question: Why do so many organizations struggle to use data when everyone knows how crucial it is to remain competitive today?
One of the biggest challenges is that there is simply too much data – and too many data sources – for the typical organization to handle. In fact, data has actually created numerous obstacles.
But companies must catch up, get rid of ineffective, antiquated systems, and modernize their technology and tools to utilize their data effectively.
The bottom line: the vast majority of data is going to waste because many organizations find it difficult to process, store and manage.
Data is useless if it is not actionable. So here are four common obstacles – and suggested solutions – to make better use of your company’s data in 2021.
1. Sharing and Collaboration
It’s often difficult for companies to share and collaborate on data analytics projects due to accessibility, security and data transfer challenges. The issue is even more complex when there are remote teams that need to work together from various geographic locations.
The solution
Advanced data analytics technologies provide secure, centralized and cloud-based tools that bring assets together in one single place, making sharing big data analytics more manageable. With this strategy, companies can prevent large quantities of data from getting altered in transit, and they can save time hunting down certain pieces of critical information.
2. Paying Too Much Money
Big data projects often involve a lot of expense. For example, if you opt for an on-premises solution, consider the costs of new hardware, new IT staff and more. Additionally, while the required frameworks are open source, companies also need to invest in the development, setup, configuration and maintenance of new software. On the other hand, if your organization decides to use a cloud-based big data solution, you’ll still need to hire staff, invest in cloud services, big data solution development and more. Furthermore, in each scenario, plan for future expansions to circumvent big data growth getting out of control and costing your organization even more money.
The solution
To save money, first determine your company’s particular technological needs and business goals. While some organizations want the flexibility benefits of the cloud, other companies with harsh security requirements require an on-premises solution.
There are also hybrid solutions available – parts of data are stored and processed in the cloud and some portions stored on-premises. This option can be a great, cost-effective strategy that can address compliance and security too.
Further reading: Databricks’ $1 Billion Funding Round Puts Focus on Data Management, AI
3. Managing Data Quality Can Be Incredibly Complex
For starters, many companies often run into the issue of data integration, especially because much of the data that needs to be analyzed comes from disparate sources in a variety of different formats – such as social media, website logs, call centers and so on. Data formats generated from these sources all differ and matching them can be an issue. There’s also the topic of unreliable data. While data isn’t 100% accurate (and isn’t required to be), it doesn’t mean that you shouldn’t strive for reliability. For instance, lots of data can include wrong information, duplicate itself or contain contradictions.
The solution
Fortunately, companies can go through the exercise of cleansing data if there are true concerns. But before this step, your data must have a proper model. Then, after creating this model, next steps can include comparing your data to the single point of truth and matching records and merging them, when applicable.
4. Security Issues
Essentially, big data security is a term for all the measures and tools companies use to protect both data and analytics methods from attacks or other malicious activities that could negatively impact them.
The problem is that, oftentimes big data adoption projects put security on the back burner until later planning stages. But this can be extremely problematic when cast aside. Successful organizations must view data security as a continuous responsibility that needs to become a part of regular business.
The solution
There’s a reason they say, “security first.” And security is especially critical at the stage of designing your data analytics solution’s foundation.
Securing data requires a holistic approach to protect companies from a complex threat across diverse systems. Fortunately, newer real-time, AI-enabled analytics tools can help organizations stay ahead of the biggest cybersecurity challenges – from detecting threats as they emerge to maintaining compliance across distributed networks and high-volume data sets. Many of these tools offer features that deliver system-wide visibility and can solve issues like failing to contain threats before they spread, saving companies significant time and money.
Final Thoughts
The growing use of data analytics among enterprises makes finding connections and trends much easier, as well as taking action based on them. In fact, in today’s data-driven environment, your organization’s success lies in being able to leverage the data it has on hand and use it as a foundation to make informed business decisions that deliver value.
Companies must learn to leverage their data, as it can be an absolute game-changer. Alternatively, organizations may be restricted to making decisions based on team members’ gut feelings versus actual facts and figures.
